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Sprint projectSep 13, 2026Honolulu, Hawaii

Citizen Evidence Triage Kit

Andrew Chang

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Citizen Evidence Triage Kit mobilizes volunteers to help AI safety researchers find the signals that matter in enormous incident datasets. The Kit gives citizen investigators a rigorous way to review behavioral traces, surface evidence-bound leads, and escalate them to experts, while leaving safety conclusions to professional researchers.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.
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  1. A thoughtful and usable prototype for a real evidence-triage bottleneck. A full volunteer study is unrealistic during a hackathon, but a small blinded pilot with 3–5 convenience users, seeded signals, and a keyword-search baseline could already test whether the workflow improves detection or saves time.

  2. This is a fantastic project! I love the idea, and it answers the question that a lot of people have when reading the news: "what can I do?" I'd encourage the author to keep working on it. The first question that popped in my mind: what happens as the frontier models increasingly communicate in languages humans don't understand?

Cite this project

@misc{chang2026citizen,
  title = {{Citizen Evidence Triage Kit}},
  author = {Andrew Chang},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/citizen-evidence-triage-kit-t6mp}},
  url = {https://apartresearch.com/sprints/projects/citizen-evidence-triage-kit-t6mp}
}

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